Fast freeform source and mask co-optimization method

ABSTRACT

The present disclosure relates to lithographic apparatuses and processes, and more particularly to tools for optimizing illumination sources and masks for use in lithographic apparatuses and processes. According to certain aspects, the present disclosure significantly speeds up the convergence of the optimization by allowing direct computation of gradient of the cost function. According to other aspects, the present disclosure allows for simultaneous optimization of both source and mask, thereby significantly speeding the overall convergence. According to still further aspects, the present disclosure allows for free-form optimization, without the constraints required by conventional optimization techniques.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/075,917, filed Nov. 8, 2013, which is a divisional of U.S. patentapplication Ser. No. 13/130,548, filed May 20, 2011 (now U.S. Pat. No.8,584,056), which is the national stage of PCT Patent Application No.PCT/US2009/065359, filed Nov. 20, 2009, which claims priority of U.S.Provisional Patent Application No. 61/116,788, filed Nov. 21, 2008, eachof which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to lithographic apparatuses andprocesses, and more particularly to tools for optimizing illuminationsources and masks for use in lithographic apparatuses and processes.

BACKGROUND OF THE RELATED ART

Lithographic apparatuses can be used, for example, in the manufacture ofintegrated circuits (ICs). In such a case, the mask may contain acircuit pattern corresponding to an individual layer of the IC, and thispattern can be imaged onto a target portion (e.g. comprising one or moredies) on a substrate (silicon wafer) that has been coated with a layerof radiation-sensitive material (resist). In general, a single waferwill contain a whole network of adjacent target portions that aresuccessively irradiated via the projection system, one at a time. In onetype of lithographic projection apparatus, each target portion isirradiated by exposing the entire mask pattern onto the target portionin one go; such an apparatus is commonly referred to as a wafer stepper.In an alternative apparatus, commonly referred to as a step-and-scanapparatus, each target portion is irradiated by progressively scanningthe mask pattern under the projection beam in a given referencedirection (the “scanning” direction) while synchronously scanning thesubstrate table parallel or anti-parallel to this direction. Since, ingeneral, the projection system will have a magnification factor M(generally <1), the speed Vat which the substrate table is scanned willbe a factor M times that at which the mask table is scanned. Moreinformation with regard to lithographic devices as described herein canbe gleaned, for example, from U.S. Pat. No. 6,046,792, incorporatedherein by reference.

In a manufacturing process using a lithographic projection apparatus, amask pattern is imaged onto a substrate that is at least partiallycovered by a layer of radiation-sensitive material (resist). Prior tothis imaging step, the substrate may undergo various procedures, such aspriming, resist coating and a soft bake. After exposure, the substratemay be subjected to other procedures, such as a post-exposure bake(PEB), development, a hard bake and measurement/inspection of the imagedfeatures. This array of procedures is used as a basis to pattern anindividual layer of a device, e.g., an IC. Such a patterned layer maythen undergo various processes such as etching, ion-implantation(doping), metallization, oxidation, chemo-mechanical polishing, etc.,all intended to finish off an individual layer. If several layers arerequired, then the whole procedure, or a variant thereof, will have tobe repeated for each new layer. Eventually, an array of devices will bepresent on the substrate (wafer). These devices are then separated fromone another by a technique such as dicing or sawing, whence theindividual devices can be mounted on a carrier, connected to pins, etc.

For the sake of simplicity, the projection system may hereinafter bereferred to as the “lens”; however, this term should be broadlyinterpreted as encompassing various types of projection systems,including refractive optics, reflective optics, and catadioptricsystems, for example. The radiation system may also include componentsoperating according to any of these design types for directing, shapingor controlling the projection beam of radiation, and such components mayalso be referred to below, collectively or singularly, as a “lens”.Further, the lithographic apparatus may be of a type having two or moresubstrate tables (and/or two or more mask tables). In such “multiplestage” devices the additional tables may be used in parallel, orpreparatory steps may be carried out on one or more tables while one ormore other tables are being used for exposures. Twin stage lithographicapparatus are described, for example, in U.S. Pat. No. 5,969,441,incorporated herein by reference.

The photolithographic masks referred to above comprise geometricpatterns corresponding to the circuit components to be integrated onto asilicon wafer. The patterns used to create such masks are generatedutilizing CAD (computer-aided design) programs, this process often beingreferred to as EDA (electronic design automation). Most CAD programsfollow a set of predetermined design rules in order to create functionalmasks. These rules are set by processing and design limitations. Forexample, design rules define the space tolerance between circuit devices(such as gates, capacitors, etc.) or interconnect lines, so as to ensurethat the circuit devices or lines do not interact with one another in anundesirable way. The design rule limitations are typically referred toas “critical dimensions” (CD). A critical dimension of a circuit can bedefined as the smallest width of a line or hole or the smallest spacebetween two lines or two holes. Thus, the CD determines the overall sizeand density of the designed circuit. Of course, one of the goals inintegrated circuit fabrication is to faithfully reproduce the originalcircuit design on the wafer (via the mask).

As noted, microlithography is a central step in the manufacturing ofsemiconductor integrated circuits, where patterns formed onsemiconductor wafer substrates define the functional elements ofsemiconductor devices, such as microprocessors, memory chips etc.Similar lithographic techniques are also used in the formation of flatpanel displays, micro-electro mechanical systems (MEMS) and otherdevices.

As semiconductor manufacturing processes continue to advance, thedimensions of circuit elements have continually been reduced while theamount of functional elements, such as transistors, per device has beensteadily increasing over decades, following a trend commonly referred toas ‘Moore's law’. At the current state of technology, critical layers ofleading-edge devices are manufactured using optical lithographicprojection systems known as scanners that project a mask image onto asubstrate using illumination from a deep-ultraviolet laser light source,creating individual circuit features having dimensions well below 100nm, i.e. less than half the wavelength of the projection light.

This process in which features with dimensions smaller than theclassical resolution limit of an optical projection system are printed,is commonly known as low-k₁ lithography, according to the resolutionformula CD=k₁×λ/NA, where λ is the wavelength of radiation employed(currently in most cases 248 nm or 193 nm), NA is the numerical apertureof the projection optics, CD is the ‘critical dimension’—generally thesmallest feature size printed—and k₁ is an empirical resolution factor.In general, the smaller k₁, the more difficult it becomes to reproduce apattern on the wafer that resembles the shape and dimensions planned bya circuit designer in order to achieve particular electricalfunctionality and performance. To overcome these difficulties,sophisticated fine-tuning steps are applied to the projection system aswell as to the mask design. These include, for example, but not limitedto, optimization of NA and optical coherence settings, customizedillumination schemes, use of phase shifting masks, optical proximitycorrection in the mask layout, or other methods generally defined as‘resolution enhancement techniques’ (RET).

As one important example, optical proximity correction (OPC, sometimesalso referred to as ‘optical and process correction’) addresses the factthat the final size and placement of a printed feature on the wafer willnot simply be a function of the size and placement of the correspondingfeature on the mask. It is noted that the terms ‘mask’ and ‘reticle’ areutilized interchangeably herein. For the small feature sizes and highfeature densities present on typical circuit designs, the position of aparticular edge of a given feature will be influenced to a certainextent by the presence or absence of other adjacent features. Theseproximity effects arise from minute amounts of light coupled from onefeature to another. Similarly, proximity effects may arise fromdiffusion and other chemical effects during post-exposure bake (PEB),resist development, and etching that generally follow lithographicexposure.

In order to ensure that the features are generated on a semiconductorsubstrate in accordance with the requirements of the given targetcircuit design, proximity effects need to be predicted utilizingsophisticated numerical models, and corrections or pre-distortions needto be applied to the design of the mask before successful manufacturingof high-end devices becomes possible. The article “Full-Chip LithographySimulation and Design Analysis—How OPC Is Changing IC Design”, C.Spence, Proc. SPIE, Vol. 5751, pp 1-14 (2005) provides an overview ofcurrent ‘model-based’ optical proximity correction processes. In atypical high-end design almost every feature edge requires somemodification in order to achieve printed patterns that come sufficientlyclose to the target design. These modifications may include shifting orbiasing of edge positions or line widths as well as application of‘assist’ features that are not intended to print themselves, but willaffect the properties of an associated primary feature.

The application of model-based OPC to a target design requires goodprocess models and considerable computational resources, given the manymillions of features typically present in a chip design. However,applying OPC is generally not an ‘exact science’, but an empirical,iterative process that does not always resolve all possible weaknesseson a layout. Therefore, post-OPC designs, i.e. mask layouts afterapplication of all pattern modifications by OPC and any other RET's,need to be verified by design inspection, i.e. intensive full-chipsimulation using calibrated numerical process models, in order tominimize the possibility of design flaws being built into themanufacturing of a mask set. This is driven by the enormous cost ofmaking high-end mask sets, which run in the multi-million dollar range,as well as by the impact on turn-around time by reworking or repairingactual masks once they have been manufactured.

Both OPC and full-chip RET verification may be based on numericalmodeling systems and methods as described, for example in, U.S. patentapplication Ser. No. 10/815,573 and an article titled “OptimizedHardware and Software For Fast, Full Chip Simulation”, by Y. Cao et al.,Proc. SPIE, Vol. 5754, 405 (2005).

In addition to performing the foregoing mask adjustments (e.g., OPC) inan effort to optimize the imaging results, the illumination schemeutilized in the imaging process can be also optimized, either jointlywith mask optimization or separately, in an effort to improve theoverall lithography fidelity. Since the 1990s, many off-axis lightsources, such as annular, quadrupole, and dipole, have been introduced,and have provided more freedom for OPC design, thereby improving theimaging results. As is known, off-axis illumination is a proven way toresolve fine structures (i.e., target features) contained in the mask,However, when compared to a traditional illuminator, an off-axisilluminator usually provides less light intensity for the aerial image(AI). Thus, it becomes necessary to attempt to optimize the illuminatorto achieve the optimal balance between finer resolution and reducedlight intensity.

Numerous prior art illumination optimization approaches are known. Forexample, in an article by Rosenbluth et al., titled “Optimum Mask andSource Patterns to Print A Given Shape”, Journal of Microlithography,Microfabrication, Microsystems 1(1), pp. 13-20, (2002), the source ispartitioned into several regions, each of which corresponds to a certainregion of the pupil spectrum. Then, the source distribution is assumedto be uniform in each source region and the brightness of each region isoptimized for process window, However, such an assumption that thesource distribution is uniform in each source region is not alwaysvalid, and as a result the effectiveness of this approach suffers. Inanother example set forth in an article by Granik, titled “SourceOptimization for Image Fidelity and Throughput”, Journal ofMicrolithography, Microfabrication, Microsystems 3(4), pp. 509-522,(2004), several existing source optimization approaches are overviewedand a method based on illuminator pixels is proposed that converts thesource optimization problem into a series of non-negative least squareoptimizations. Though these methods have demonstrated some successes,they typically require multiple complicated iterations to converge. Inaddition, it may be difficult to determine the appropriate/optimalvalues for some extra parameters, such as γ in Granik's method, whichdictates the trade-off between optimizing the source for wafer imagefidelity and the smoothness requirement of the source.

For low k1 photolithography, optimization of both source and mask iscritical to ensure a viable process window for printing criticalpatterns. Existing algorithms (e.g. Socha et. al. Proc. SPIE vol. 5853,2005, p. 180) cannot perform simultaneous optimization of both sourceand mask. Rather, they generally discretize illumination intoindependent source points and mask into diffraction orders in thespatial frequency domain, and separately formulate a cost function basedon process window metrics such as exposure latitude which could bepredicted by optical imaging models from source point intensities andmask diffraction orders. Then standard optimization techniques are usedto minimize the objective function.

One problem with these existing algorithms that formulate a costfunction is that they require a large number of full forward opticalimaging model simulations before convergence on both optimal source andmask is reached. A clip of medium complexity would therefore take weeksor even months to optimize on latest standard PC hardware. However, aproduct is generally not considered practicable unless the time requiredis less than about 24 hours.

Relatedly, the delay of EUV lithography and the pressure of everdecreasing design rules have driven semiconductor chipmakers to movedeeper into the low k₁ lithography era with existing 193 nm ArFlithography. Lithography towards lower k₁ puts heavy demands onresolution enhancement techniques (RET), exposure tools, and the needfor litho-friendly design. The 1.35 ArF hyper numerical apertures (NA)exposure tool will be the exposure tool for chip manufactures to use inthe next two years. To ensure that the design can be printed withworkable process window; source-mask optimization (SMO) is becoming animportant RET that is required for 2×nm node.

As such, there is a need for a source illumination and mask optimizationmethod and system so as to allow for simultaneous optimization of thesource and mask using a cost function without constraints and within apracticable amount of time.

SUMMARY

The present disclosure relates to lithographic apparatuses andprocesses, and more particularly to tools for optimizing illuminationsources and masks for use in lithographic apparatuses and processes.According to certain aspects, certain embodiments of the presentdisclosure significantly speed up the convergence of the optimization byallowing direct computation of gradient of a wide class of costfunctions. According to other aspects, certain embodiments of thepresent disclosure allow for simultaneous optimization of both sourceand mask, thereby significantly speeding the overall convergence andimproving the final result. According to still further aspects, certainembodiments of the present disclosure allow for free-form optimization,without the constraints required by conventional optimizationtechniques, and discovers the full process window entitlement absentmanufacturability constraints. According to yet additional aspects,certain embodiments of the present disclosure employ a method to placesub-resolution assist feature (SRAF) seeds according to free-formoptimization results and grows these SRAF seeds while performing mainfeature optical proximity correction (OPC) in a subsequent constrainedoptimization that takes into account manufacturability constraints fromboth source and mask sides. According to further aspects, certainembodiments of the present disclosure utilize a cost function tominimize the worst edge placement errors across process window, and acomputationally friendly approximation to such a cost function.

In furtherance of these and other aspects, a method for optimizing alithographic process according to certain embodiments includes receivingdescriptions of an illumination source and a mask, the mask comprising alithography pattern, and until the source and mask are simultaneouslyoptimized for a process window of the lithographic process, selectivelyrepeating the steps of: forming a cost function as a function of boththe illumination source and mask, calculating the gradient of the costfunction, and reconfiguring the source and mask descriptions dependingon the calculated gradient.

In additional furtherance of these and other aspects, a method foroptimizing a lithographic process having an illumination source and amask according to certain embodiments includes forming a cost functionas a function of descriptions of both the illumination source and mask,wherein the cost function is formulated in terms of worst case edgeplacement error over a given process window, and calculating a gradientof the cost function.

In yet additional furtherance of these and other aspects, a method foroptimizing a lithographic process having an illumination source and amask according to certain embodiments includes a free-form optimizationprocess, placing SRAF seeds in a description of the mask based on aresult of the free-form optimization process, and a constrainedoptimization process, including growing the SRAF seeds while taking intoaccount manufacturability constraints for both the illumination sourceand the mask.

Although specific reference may be made in this text to the use of theembodiments in the manufacture of ICs, it should be explicitlyunderstood that the disclosure has many other possible applications. Forexample, it may be employed in the manufacture of integrated opticalsystems, guidance and detection patterns for magnetic domain memories,liquid-crystal display panels, thin-film magnetic heads, etc. Theskilled artisan will appreciate that, in the context of such alternativeapplications, any use of the terms “reticle”, “wafer” or “die” in thistext should be considered as being replaced by the more general terms“mask”, “substrate” and “target portion”, respectively.

In the present document, the terms “radiation” and “beam” are used toencompass all types of electromagnetic radiation, including ultravioletradiation (e.g. with a wavelength of 365, 248, 193, 157 or 126 nm) andEUV (extreme ultra-violet radiation, e.g. having a wavelength in therange 5-20 nm).

The term mask as employed in this text may be broadly interpreted asreferring to generic patterning means that can be used to endow anincoming radiation beam with a patterned cross-section, corresponding toa pattern that is to be created in a target portion of the substrate;the term “light valve” can also be used in this context. Besides theclassic mask (transmissive or reflective; binary, phase-shifting,hybrid, etc.), examples of other such patterning means include:

-   -   a programmable mirror array. An example of such a device is a        matrix-addressable surface having a viscoelastic control layer        and a reflective surface. The basic principle behind such an        apparatus is that (for example) addressed areas of the        reflective surface reflect incident light as diffracted light,        whereas unaddressed areas reflect incident light as undiffracted        light. Using an appropriate filter, the said undiffracted light        can be filtered out of the reflected beam, leaving only the        diffracted light behind; in this manner, the beam becomes        patterned according to the addressing pattern of the        matrix-addressable surface. The required matrix addressing can        be performed using suitable electronic means. More information        on such mirror arrays can be gleaned, for example, from U.S.        Pat. No. 5,296,891 and U.S. Pat. No. 5,523,193, which are        incorporated herein by reference.    -   a programmable LCD array. An example of such a construction is        given in U.S. Pat. No. 5,229,872, which is incorporated herein        by reference.

The embodiments, together with further objects and advantages, can bebetter understood by reference to the following detailed description andthe accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments will now be described, by way of example only, withreference to the accompanying schematic drawings in which correspondingreference symbols indicate corresponding parts, and in which:

FIG. 1 is an exemplary block diagram illustrating a typical lithographicprojection system.

FIG. 2 is an exemplary block diagram illustrating the functional modulesof a lithographic simulation model.

FIG. 3 is a schematic depiction of the general optimization processemployed in certain aspects of the disclosure.

FIG. 4 is a chart illustrating a source and continuous transmission maskco-optimization flow (CTM flow) according to additional embodiments.

FIG. 5 illustrates a resultant source and mask for an exampleapplication of a design for a DRAM.

FIG. 6 illustrates a converted “New” illuminator and DOE sourceaccording to an example application of the disclosure.

FIGS. 7A and 7B illustrate example masks that result with a DOE sourceand “New” illuminator according to applications of the disclosure.

FIG. 8 is a block diagram that illustrates a computer system which canassist in the implementation of the simulation method of the presentdisclosure.

FIG. 9 schematically depicts a lithographic projection apparatussuitable for use with the method of the present disclosure.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

Prior to discussing the present embodiments, a brief discussionregarding the overall simulation and imaging process is provided. FIG. 1illustrates an exemplary lithographic projection system 10. The majorcomponents are a light source 12, which may be a deep-ultravioletexcimer laser source, illumination optics which define the partialcoherence (denoted as sigma) and which may include specific sourceshaping optics 14, 16 a and 16 b; a mask or reticle 18; and projectionoptics 16 c that produce an image of the reticle pattern onto the waferplane 22. An adjustable filter or aperture 20 at the pupil plane mayrestrict the range of beam angles that impinge on the wafer plane 22,where the largest possible angle defines the numerical aperture of theprojection optics NA=sin(Θ_(max)).

In a lithography simulation system, these major system components can bedescribed by separate functional modules, for example, as illustrated inFIG. 2. Referring to FIG. 2, the functional modules include the designlayout module 26, which defines the target design; the mask layoutmodule 28, which defines the mask to be utilized in the imaging process;the mask model module 30, which defines the model of the mask layout tobe utilized during the simulation process; the optical model module 32,which defines the performance of the optical components of lithographysystem; and the resist model module 34, which defines the performance ofthe resist being utilized in the given process. As is known, the resultof the simulation process produces, for example, predicted contours andCDs in the result module 36.

More specifically, it is noted that the properties of the illuminationand projection optics are captured in the optical model 32 thatincludes, but not limited to, NA-sigma (σ) settings as well as anyparticular illumination source shape (e.g. off-axis light sources suchas annular, quadrupole, and dipole, etc.). The optical properties of thephoto-resist layer coated on a substrate—i.e. refractive index, filmthickness, propagation and polarization effects—may also be captured aspart of the optical model 32. The mask model 30 captures the designfeatures of the reticle and may also include a representation ofdetailed physical properties of the mask, as described, for example, inU.S. Pat. No. 7,587,704. Finally, the resist model 34 describes theeffects of chemical processes which occur during resist exposure, PEBand development, in order to predict, for example, contours of resistfeatures formed on the substrate wafer. The objective of the simulationis to accurately predict, for example, edge placements and CDs, whichcan then be compared against the target design. The target design, isgenerally defined as the pre-OPC mask layout, and will be provided in astandardized digital file format such as GDSII or OASIS.

In a typical high-end design almost every feature edge requires somemodification in order to achieve printed patterns that come sufficientlyclose to the target design. These modifications may include shifting orbiasing of edge positions or line widths as well as application of‘assist’ features that are not intended to print themselves, but willaffect the properties of an associated primary feature. Furthermore,optimization techniques applied to the source of illumination may havedifferent effects on different edges and features. Optimization ofillumination sources can include the use of pupils to restrict sourceillumination to a selected pattern of light. The present disclosureprovides optimization methods that can be applied to both source andmask configurations simultaneously.

With reference to a high-level block diagram in FIG. 3, certainembodiments of the present disclosure provide methods for acceleratedand simultaneous optimization of mask and source. Initial source 320 andmask 322 configurations (e.g. corresponding to optical model 32 and maskmodel 30 described above, respectively) are supplied to an optimizationmodule 324. Optimization module 324 comprises an iterative optimizerthat calculates a cost function and a gradient for each iteration. At340, a cost function for the mask and source is evaluated for eachiteration. The gradient of the cost function can then be examined at 342to determine if convergence has been obtained. If the gradient isnon-zero, then it may be considered that convergence has not beenachieved and changes to source and mask can be calculated and applied at344 before repeating the steps of computing a cost function and gradientfor the new mask and source at 340 and testing for convergence 342. Whenconvergence has been achieved, the final source 326 and mask 326 areconsidered to be optimized.

Changes to the source and mask in 344 can be calculated and/or performedin a variety of ways, and it is not necessary for the exact sequenceshown in FIG. 3 to be followed in all embodiments. For example, optimalresults can be obtained by performing an unconstrained (or significantlyless constrained) optimization followed by a fully constrainedoptimization step. The relative more freedom in the unconstrained (orless constrained) optimization step means it is likely to reach anoptimal solution in a global sense. The fully constrained optimizationwould then start from an initial condition derived from this solution.

The unconstrained (or less constrained) optimization can be performed inthe space of free-form source and free-form mask. A free-form source isrepresented as a source intensity map on a sampling grid in the sourcepupil plane, and pixel values of the map are allowed to vary freely.Similarly, a free-form mask is the mask transmission map on a samplinggrid with each pixel value free to vary. Free-form optimization permitsfaster calculation of the gradient of the cost function and certainalgorithms can be selected to accelerate the achievement of an optimalsolution.

The initial condition of the fully constrained optimization can beconstructed from the free-form result via a seeding process. Thefree-form mask result would serve as a guide as to potential locationsto insert sub-resolution assist features (SRAF). Small SRAF seeds arethen placed at these locations and are allowed to grow or shrink or moveduring the optimization. The main features of the mask design are alsoco-optimized along with the SRAF seeds to achieve the best solution.Similarly, the free-form source result could also be used to selectcandidates for the final illumination solutions, for example, ones basedon diffractive optical elements (DOE). These source solution candidatescould also be simultaneously optimized with main and SRAF mask features.

Referring back to step 340 in FIG. 3, certain aspects of the disclosureinclude significantly speeding up the convergence of the optimization byallowing direct computation of the gradient of the cost function. Themethods include the use of linearized functions selected to optimize theprinted wafer contour throughout the process window. The cost functionis typically based on a pure optical model because optics inphotolithography systems tend to determine a majority of the processconstraints. In one example, the cost function may be selected to reducethe worst edge placement error of a design layout throughout the processwindow. Mathematically, the cost function F may be written as:

$F = {\max\limits_{pw}\; {\max\limits_{e}\; {{EPE}\left( {{pw},e} \right)}}}$

where pw is a list of process window conditions and variable e runs overa set of evaluation points placed along the target design layout.

This cost function could be transformed into a more computationallyefficient form by employing the following approximations.

First, EPE is approximated by a linearized approximation,

${{{EPE}\left( {{pw},e} \right)} \approx \frac{\left\lbrack {{I_{pw}(e)} - I_{th}} \right\rbrack}{{\nabla I_{pw}}}},$

where I_(pw)(e) denotes the aerial image intensity at process windowcondition pw, and I_(th) the threshold for the aerial image contour. Thedenominator, ∥∇I_(pw)∥, represents the slope of aerial image.

Next, the max operator is approximated by an L_(p) norm,

${F^{p} \approx {\sum\limits_{pw}^{\;}\; {\sum\limits_{e}^{\;}\; {{EPE}^{p}\left( {{pw},e} \right)}}}},$

with p a positive integer. The bigger the value of p, the better thisapproximation is.

Putting everything together, we have this new cost function,

${F = {\sum\limits_{pw}^{\;}\; {\sum\limits_{x}^{\;}\; {{w\left( {{pw},x} \right)}\frac{\left\lbrack {{I_{pw}(x)} - I_{th}} \right\rbrack^{p}}{{{\nabla I_{pw}}}^{p}}}}}},{p \in {N.}}$

As can be seen, a weighting factor w(pw,e) is also preferably introducedto provide extra flexibility to control the goal of the optimization,which could be determined from considerations like evaluation pointlocation (e.g. line, line end, jog) or relevant feature size (e.g, linewidth, space), or process window position.

Those skilled in the art will recognize many ways how the masktransmissions M(x) and source intensities S(s) can be derived from thereceived source and mask descriptions (e.g. pixel-based mapscorresponding to mask model 30 and optical model 32, respectively), andso details thereof will be omitted here for the sake of clarity of thedisclosure. The present inventors recognize that aerial intensity I canbe regarded as a function of mask transmissions M(x) and sourceintensities S(s), and therefore so can the cost function F. The costfunction may be expanded using a Taylor series and, in certainembodiments, the floor of the gradient may be discovered using firstorder terms. More particularly, F may be expressed as:

F=F[I(M(x), S(s))]=F[M(x), S(s)]

This cost function may be minimized using any of a variety of knownalgorithms when the gradient or derivatives of F are computed withrespect to M and S:

$F \approx {{F\left\lbrack {M_{0},S_{0}} \right\rbrack} + {\sum\limits_{x}^{\;}\; {\frac{\delta \; F}{\delta \; {M(x)}}\left( {{M(x)} - {M_{0}(x)}} \right)}} + {\sum\limits_{s}^{\;}\; {\frac{\delta \; F}{\delta \; {S(s)}}\left( {{S(s)} - {S_{0}(s)}} \right)}}}$

The derivatives of aerial image intensity I with respect to M and S, andby the chain rule, derivatives of F can be efficiently computed and thetime to compute all derivatives is on the same order of magnitude as asingle aerial image computation. The aerial image is the summation ofcontribution from each source point, and its variation with respect tothe source map is the single contribution:

I[M(x), S(s)] = ∫sS(s)I_(s)[M(x)]$\frac{\delta \; {I\left\lbrack {{M(x)},{S(s)}} \right\rbrack}}{\delta \; {S\left( s^{\prime} \right)}} = {{I_{s}\left\lbrack {M(x)} \right\rbrack}{\delta \left( {s - s^{\prime}} \right)}}$

The aerial image can also be expressed in Hopkins formulation as a sumof coherence systems:

$\begin{matrix}{{I\left\lbrack {{M(x)},{S(s)}} \right\rbrack} = {\int{{x^{\prime}}{x^{''}}{M\left( x^{\prime} \right)}{J_{s}\left( {{x - x^{\prime}},{x - x^{''}}} \right)}{M^{*}\left( x^{''} \right)}}}} \\{= {{\sum\limits_{k}{\lambda_{k}{{\int{{x^{\prime}}{V_{k}\left( {x - x^{\prime}} \right)}{M\left( x^{\prime} \right)}}}}^{2}}} \equiv {\sum\limits_{k}^{\;}\; {\lambda_{k}{{V_{k} \otimes M}}^{2}}}}} \\{\frac{\delta \; {I\left\lbrack {{M(x)},{S(s)}} \right\rbrack}}{\delta \; {M\left( x^{\prime} \right)}} = {{\sum\limits_{k}^{\;}\; {\lambda_{k}{V_{k}\left( {x - x^{\prime}} \right)}{\int{{x^{''}}{V_{k}^{*}\left( {x - x^{''}} \right)}{M^{*}\left( x^{''} \right)}}}}} + {c.c.}}}\end{matrix}$

Where “c.c.” represents the complex conjugate.

Having determined the aerial image variations, the variations of thecost function itself as a function of the aerial image can be computedas follows:

F = F[I[M(x)S(s)]] $\begin{matrix}{\frac{\delta \; F}{\delta \; {M(x)}} = {\int{{x^{\prime}}\frac{\delta \; F}{\delta \; {I\left( x^{\prime} \right)}}\frac{\delta \; {I\left( {x^{\prime},s} \right)}}{\delta \; {M(x)}}}}} \\{{= {\sum\limits_{k}\; {\lambda_{k}{\int{{x^{\prime}}\frac{\delta \; F}{\delta \; {I\left( x^{\prime} \right)}}{V_{k}\left( {x^{\prime} - x} \right)}{\int{{x^{''}}{V_{k}^{*}\left( {x^{\prime} - x^{''}} \right)}}}}}}}}} \\{{{M^{*}\left( x^{''} \right)} + {c.c.}}} \\{{= {{\sum\limits_{k}\; {\lambda_{k}\left( {{\hat{V}}_{k} \otimes \left( {\frac{\delta \; F}{\delta \; I}\left( {V_{k} \otimes M} \right)^{*}} \right)} \right)}} + {c.c.}}},{{{\hat{V}}_{k}(x)} \equiv {V_{k}\left( {- x} \right)}}}\end{matrix}$$\frac{\delta \; F}{\delta \; {S(s)}} = {{\int{{s^{\prime}}{\int{{x^{\prime}}\frac{\delta \; F}{\delta \; {I\left( x^{\prime} \right)}}\frac{\delta \; {I\left( {x^{\prime},s^{\prime}} \right)}}{\delta \; {S(s)}}}}}} = {\int{{x^{\prime}}\frac{\delta \; F}{\delta \; {I\left( x^{\prime} \right)}}{I_{s}\left( {x^{\prime},s} \right)}}}}$

According to aspects of the disclosure that can be ascertained from theabove, the variation with respect to mask image can be computed as aseries of convolutions, thereby providing means for significantlydecreasing computation time. The variation of the cost function withrespect to the aerial image itself may be computed and the form of thecost function may be written:

F=F[I(x)]=∫dx w(x) f(I(x), ∇I(x)).

In this case, the variation would be:

$\frac{\delta \; F}{\delta \; {I(x)}} = {{{w(x)}\frac{\partial f}{\partial I}} - {\nabla{\cdot \left( {{w(x)}\frac{\partial f}{\partial{\nabla I}}} \right)}}}$

Thus, variations of the cost function with respect to both source andmask can be simultaneously obtained. In the free-form source and maskoptimization these variations become the gradient of the cost function.Thereafter, any suitable gradient-based optimization technique can beapplied to find a minimum of the cost function.

The descriptions above provide an example embodiment where the costfunction is based on EPE. Examples of other cost functions include (1)the EPE least square function, (2) the EPE least p-norm function where pis even and greater than 2, (3) the inverse NILS p-norm function, (4)the contour integral of image slope with M as the design target, (5) theedge image value least square, (6) the edge image p-norm (p is evenand >2) and (7) the ILS p-norm with F to be maximized. The sevencorresponding cost function equations are listed below:

$\begin{matrix}{F = {\sum\limits_{pw}\; {\sum\limits_{x}\; {{w\left( {{pw},x} \right)}\frac{\left\lbrack {{I_{pw}(x)} - I_{th}} \right\rbrack^{2}}{{{\nabla I_{pw}}}^{2}}}}}} & (1) \\{F = {\sum\limits_{pw}\; {\sum\limits_{x}\; {{w\left( {{pw},x} \right)}\frac{\left\lbrack {{I_{pw}(x)} - I_{th}} \right\rbrack^{p}}{{{\nabla I_{pw}}}^{p}}}}}} & (2) \\{F = {\sum\limits_{pw}\; {\sum\limits_{x}\; {{w\left( {{pw},x} \right)}\frac{\left\lbrack {I_{pw}(x)} \right\rbrack^{p}}{{{{CD}_{x}{\nabla I_{pw}}}}^{p}}}}}} & (3) \\\begin{matrix}{{F = {- {\sum\limits_{pw}\; {\oint\limits_{\partial M}{{{{lw}\left( {{pw},x} \right)}}\left( {\hat{n} \cdot {\nabla I_{pw}}} \right)}}}}}\;} \\{= {- {\sum\limits_{pw}\; {\underset{M}{\int\int}{S}{\nabla{\cdot \left( {{w\left( {{pw},x} \right)}{\nabla I_{pw}}} \right)}}}}}}\end{matrix} & (4) \\{F = {\sum\limits_{pw}\; {\sum\limits_{x}\; {{w\left( {{pw},x} \right)}\left\lbrack {{I_{pw}(x)} - I_{th}} \right\rbrack}^{2}}}} & (5) \\{F = {\sum\limits_{pw}\; {\sum\limits_{x}\; {{w\left( {{pw},x} \right)}\left\lbrack {{I_{pw}(x)} - I_{th}} \right\rbrack}^{p}}}} & (6) \\{F = {\sum\limits_{pw}\; {\sum\limits_{x}\; {{w\left( {{pw},x} \right)}\frac{{{\nabla I_{pw}}}^{p}}{{CD}_{x}}}}}} & (7)\end{matrix}$

One skilled in the art would fully understand how to determine theoptimized gradient for these and other cost functions based after beingtaught by the above descriptions. For example, some standardoptimization techniques utilize gradient information such as steepestdescent, conjugation gradient or quasi-Newton methods.

The gradient calculation formulae described above can be implemented invarious computing platforms. Additionally or alternatively, speciallyadapted hardware acceleration platforms can be used to further improvethe optimization speed. For example, platforms can that includespecialized digital signal processors (“DSPs”) can be employed toprocess cost functions and calculate gradients. However, it will beappreciated that calculations maybe performed on other computingplatforms that can comprise parallel processors, mathematicalcoprocessors and DSP based coprocessors.

To provide synergy between certain types of scanners and SMO solutionsto meet advanced low k₁ imaging requirements, and armed with theoptimization algorithms described above, the present inventors havedeveloped a SMO flow that can utilize fully flexible illuminators ordifferent types of application specific/custom DOEs, rather thanstandard or pre-selected illumination designs.

In this regard, FIG. 4 illustrates a source and continuous transmissionmask co-optimization flow (CTM flow) according to additional embodimentsof the disclosure. As shown in FIG. 4, the first step of the CTM flow isto set up all the input parameters for the optimization including:Model, DOE type, polarization, mask manufacture rule check (MRC) andprocess information etc. (502). For example, in the set up, a userspecifies the type of source constraints to be applied, either customDOE or fully-flexible illuminator. This will determine later how theunconstrained freeform source will be converted and co-optimized. Thesesetup parameters are used throughout the entire flow. Then, models willbe created at user-specified PW corner conditions as shown in FIG. 4(504). Users can specify the DOF versus EL trade off in this step, forexample.

With all the setup parameters, step 506 starts the co-optimization withunconstrained freeform source and continuous transmission mask, usingfor example the optimization process of optimization module 324,including the cost function and gradient calculations, freeform sourceand mask optimizations and assist feature optimizations described above.The only constraint in this stage is the upper and lower bound of maskand source transmission which has physical limitations. Withoutconstraints, optimization in this stage will search for solutions in thelargest possible solution space, and give the best possible processwindow (PW) and MEF. The resultant source 602 and mask 604 for anexample application of a design for a DRAM is shown in FIG. 5,respectively. However, neither the freeform source nor continuoustransmission mask are manufacturable. Therefore, after freeform sourceand continuous transmission mask co-optimization, for practicalpurposes, on the source side, it needs to be converted into amanufacturable source (508), such as a DOE 704 shown in FIG. 6 or a“New” (e.g. fully flexible) illuminator 702 shown in FIG. 6. On the maskside, the mask needs to be constrained to a fixed transmission value(510). Then the selected source-mask combination is co-optimized usingthe scanner illuminator and mask manufacture rule check (MRC)constraints. The “New” illuminator closely resembles the freeform source(resulting from 514), and is expected to give minimal impact on the PW(as analyzed in 516) compared to a parametric DOE source (resulting from512).

For an example application for a DRAM design, FIG. 6 shows the converted“New” illuminator 702 and DOE source 704, respectively. From theoptimized continuous transmission mask gray tone image, AF seeds areextracted and are optimized during the next stage. In the final stage,the constrained source along with the main and assist features on themask will be optimized with the same cost function as in the initialco-optimization result (512 and 514), Co-optimization is crucial in thisstep because both the source and mask manufacturability constraints cansignificantly modify the original source topology, and performs amask-only optimization which does not guarantee the optimum result.FIGS. 7A and 7B show the masks 804 and 808 that result with the DOEsource 802 and “New” illuminator 806, respectively.

In an embodiment, there is provided a computer-implemented method foroptimizing a lithographic process having an illumination source and amask, the method comprising: a free-form optimization process; placingsub-resolution assist feature (SRAF) seeds in a description of the maskbased on a result of the free-form optimization process; and aconstrained optimization process, including growing the SRAF seeds whiletaking into account manufacturability constraints for both theillumination source and the mask, wherein one or more steps areperformed by the computer.

In an embodiment, the free-form optimization process includes designingan optimal illumination source that comprises a fully flexible set ofillumination source points. In an embodiment, taking into account themanufacturability constraints for the illumination source includesmatching the optimal illumination source to a diffractive opticalelement. In an embodiment, taking into account the manufacturabilityconstraints for the mask includes constraining a mask transmission to apredetermined value. In an embodiment, the constrained optimizationprocess includes iteratively converging a cost function, wherein thecost function comprises a function of both the illumination source andthe mask. In an embodiment, the cost function is formulated in terms ofone of the following: worst case edge placement error (EPE) over a givenprocess window, EPE least square function, EPE least p-norm function,inverse NILS p-norm function, contour integral image slop, edge imagevalue least square, edge image p-norm, and ILS p-norm. In an embodiment,the convergence of the cost function is accelerated by directlycomputing a gradient of the cost function in each iteration with respectof the mask and the source. In an embodiment, the source and the maskare reconfigured for each iteration, until the gradient is at a desiredminimum value.

In an embodiment, there is provided a computer program productcomprising a non-transitory computer readable medium having instructionsrecorded thereon, the instructions, when executed by a computer,implements a method for optimizing a lithographic process having anillumination source and a mask, the method comprising: a free-formoptimization process; placing sub-resolution assist feature (SRAF) seedsin a description of the mask based on a result of the free-formoptimization process; and a constrained optimization process, includinggrowing the SRAF seeds while taking into account manufacturabilityconstraints for both the illumination source and the mask.

In an embodiment, the free-form optimization process includes designingan optimal illumination source that comprises a fully flexible set ofillumination source points. In an embodiment, taking into account themanufacturability constraints for the illumination source includesmatching the optimal illumination source to a diffractive opticalelement. In an embodiment, taking into account the manufacturabilityconstraints for the mask includes constraining a mask transmission to apredetermined value. In an embodiment, the constrained optimizationprocess includes iteratively converging a cost function, wherein thecost function comprises a function of both the illumination source andthe mask. In an embodiment, the cost function is formulated in terms ofone of the following: worst case edge placement error (EPE) over a givenprocess window, EPE least square function, EPE least p-norm function,inverse NILS p-norm function, contour integral image slop, edge imagevalue least square, edge image p-norm, and ILS p-norm. In an embodiment,the convergence of the cost function is accelerated by directlycomputing a gradient of the cost function in each iteration with respectof the mask and the source. In an embodiment, the source and the maskare reconfigured before each iteration until the gradient is at adesired minimum value.

FIG. 8 is a block diagram that illustrates a computer system 100 whichcan assist in implementing the optimization methods and flows disclosedherein. Computer system 100 includes a bus 102 or other communicationmechanism for communicating information, and a processor 104 coupledwith bus 102 for processing information. Computer system 100 alsoincludes a main memory 106, such as a random access memory (RAM) orother dynamic storage device, coupled to bus 102 for storing informationand instructions to be executed by processor 104. Main memory 106 alsomay be used for storing temporary variables or other intermediateinformation during execution of instructions to be executed by processor104. Computer system 100 further includes a read only memory (ROM) 108or other static storage device coupled to bus 102 for storing staticinformation and instructions for processor 104. A storage device 110,such as a magnetic disk or optical disk, is provided and coupled to bus102 for storing information and instructions.

Computer system 100 may be coupled via bus 102 to a display 112, such asa cathode ray tube (CRT) or flat panel or touch panel display fordisplaying information to a computer user. An input device 114,including alphanumeric and other keys, is coupled to bus 102 forcommunicating information and command selections to processor 104.Another type of user input device is cursor control 116, such as amouse, a trackball, or cursor direction keys for communicating directioninformation and command selections to processor 104 and for controllingcursor movement on display 112. This input device typically has twodegrees of freedom in two axes, a first axis (e.g., x) and a second axis(e.g., y), that allows the device to specify positions in a plane. Atouch panel (screen) display may also be used as an input device.

According to one embodiment of the disclosure, portions of theoptimization process may be performed by computer system 100 in responseto processor 104 executing one or more sequences of one or moreinstructions contained in main memory 106. Such instructions may be readinto main memory 106 from another computer-readable medium, such asstorage device 110. Execution of the sequences of instructions containedin main memory 106 causes processor 104 to perform the process stepsdescribed herein. One or more processors in a multi-processingarrangement may also be employed to execute the sequences ofinstructions contained in main memory 106. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions to implement the disclosure. Thus, embodiments ofthe disclosure are not limited to any specific combination of hardwarecircuitry and software.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing instructions to processor 104 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media include, for example, optical or magnetic disks, suchas storage device 110. Volatile media include dynamic memory, such asmain memory 106. Transmission media include coaxial cables, copper wireand fiber optics, including the wires that comprise bus 102.Transmission media can also take the form of acoustic or light waves,such as those generated during radio frequency (RF) and infrared (IR)data communications. Common forms of computer-readable media include,for example, a floppy disk, a flexible disk, hard disk, magnetic tape,any other magnetic medium, a CD-ROM, DVD, any other optical medium,punch cards, paper tape, any other physical medium with patterns ofholes, a RAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip orcartridge, a carrier wave as described hereinafter, or any other mediumfrom which a computer can read.

Various forms of computer readable media may be involved in carrying oneor more sequences of one or more instructions to processor 104 forexecution. For example, the instructions may initially be borne on amagnetic disk of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 100 canreceive the data on the telephone line and use an infrared transmitterto convert the data to an infrared signal. An infrared detector coupledto bus 102 can receive the data carried in the infrared signal and placethe data on bus 102. Bus 102 carries the data to main memory 106, fromwhich processor 104 retrieves and executes the instructions. Theinstructions received by main memory 106 may optionally be stored onstorage device 110 either before or after execution by processor 104.

Computer system 100 also preferably includes a communication interface118 coupled to bus 102. Communication interface 118 provides a two-waydata communication coupling to a network link 120 that is connected to alocal network 122. For example, communication interface 118 may be anintegrated services digital network (ISDN) card or a modem to provide adata communication connection to a corresponding type of telephone line.As another example, communication interface 118 may be a local areanetwork (LAN) card to provide a data communication connection to acompatible LAN. Wireless links may also be implemented. In any suchimplementation, communication interface 118 sends and receiveselectrical, electromagnetic or optical signals that carry digital datastreams representing various types of information.

Network link 120 typically provides data communication through one ormore networks to other data devices. For example, network link 120 mayprovide a connection through local network 122 to a host computer 124 orto data equipment operated by an Internet Service Provider (ISP) 126.ISP 126 in turn provides data communication services through theworldwide packet data communication network, now commonly referred to asthe “Internet” 128. Local network 122 and Internet 128 both useelectrical, electromagnetic or optical signals that carry digital datastreams. The signals through the various networks and the signals onnetwork link 120 and through communication interface 118, which carrythe digital data to and from computer system 100, are exemplary forms ofcarrier waves transporting the information.

Computer system 100 can send messages and receive data, includingprogram code, through the network(s), network link 120, andcommunication interface 118. In the Internet example, a server 130 mighttransmit a requested code for an application program through Internet128, ISP 126, local network 122 and communication interface 118. Inaccordance with the disclosure, one such downloaded application providesfor the illumination optimization of the embodiment, for example. Thereceived code may be executed by processor 104 as it is received, and/orstored in storage device 110, or other non-volatile storage for laterexecution. In this manner, computer system 100 may obtain applicationcode in the form of a carrier wave.

FIG. 9 schematically depicts an exemplary lithographic projectionapparatus whose illumination source could be optimized utilizing theprocess of present invention. The apparatus comprises:

-   -   a radiation system Ex, IL, for supplying a projection beam PB of        radiation. In this particular case, the radiation system also        comprises a radiation source LA;    -   a first object table (mask table) MT provided with a mask holder        for holding a mask MA (e.g., a reticle), and connected to first        positioning means for accurately positioning the mask with        respect to item PL;    -   a second object table (substrate table) WT provided with a        substrate holder for holding a substrate W (e.g., a        resist-coated silicon wafer), and connected to second        positioning means for accurately positioning the substrate with        respect to item PL;    -   a projection system (“lens”) PL (e.g., a refractive, catoptric        or catadioptric optical system) for imaging an irradiated        portion of the mask MA onto a target portion C (e.g., comprising        one or more dies) of the substrate W.

As depicted herein, the apparatus is of a transmissive type (i.e., has atransmissive mask). However, in general, it may also be of a reflectivetype, for example (with a reflective mask). Alternatively, the apparatusmay employ another kind of patterning means as an alternative to the useof a mask; examples include a programmable mirror array or LCD matrix.

The source LA (e.g., a mercury lamp or excimer laser) produces a beam ofradiation. This beam is fed into an illumination system (illuminator)IL, either directly or after having traversed conditioning means, suchas a beam expander Ex, for example. The illuminator IL may compriseadjusting means AM for setting the outer and/or inner radial extent(commonly referred to as σ-outer and σ-inner, respectively) of theintensity distribution in the beam. In addition, it will generallycomprise various other components, such as an integrator IN and acondenser CO. In this way, the beam PB impinging on the mask MA has adesired uniformity and intensity distribution in its cross-section.

It should be noted with regard to FIG. 9 that the source LA may bewithin the housing of the lithographic projection apparatus (as is oftenthe case when the source LA is a mercury lamp, for example), but that itmay also be remote from the lithographic projection apparatus, theradiation beam that it produces being led into the apparatus (e.g., withthe aid of suitable directing mirrors); this latter scenario is oftenthe case when the source LA is an excimer laser (e.g., based on KrF, ArFor F₂ lasing). The current disclosure encompasses at least both of thesescenarios.

The beam PB subsequently intercepts the mask MA, which is held on a masktable MT. Having traversed the mask MA, the beam PB passes through thelens PL, which focuses the beam PB onto a target portion C of thesubstrate W. With the aid of the second positioning means (andinterferometric measuring means IF), the substrate table WT can be movedaccurately, e.g. so as to position different target portions C in thepath of the beam PB. Similarly, the first positioning means can be usedto accurately position the mask MA with respect to the path of the beamPB, e.g., after mechanical retrieval of the mask MA from a mask library,or during a scan. In general, movement of the object tables MT, WT willbe realized with the aid of a long-stroke module (coarse positioning)and a short-stroke module (fine positioning), which are not explicitlydepicted in FIG. 9. However, in the case of a wafer stepper (as opposedto a step-and-scan tool) the mask table MT may just be connected to ashort stroke actuator, or may be fixed.

The depicted tool can be used in two different modes:

-   -   In step mode, the mask table MT is kept essentially stationary,        and an entire mask image is projected in one go (i.e., a single        “flash”) onto a target portion C. The substrate table WT is then        shifted in the x and/or y directions so that a different target        portion C can be irradiated by the beam PB;    -   In scan mode, essentially the same scenario applies, except that        a given target portion C is not exposed in a single “flash”.        Instead, the mask table MT is movable in a given direction (the        so-called “scan direction”, e.g., the y direction) with a speed        v, so that the projection beam PB is caused to scan over a mask        image; concurrently, the substrate table WT is simultaneously        moved in the same or opposite direction at a speed V=Mv, in        which M is the magnification of the lens PL (typically, M=¼ or        ⅕). In this manner, a relatively large target portion C can be        exposed, without having to compromise on resolution.

The concepts disclosed herein may simulate or mathematically model anygeneric imaging system for imaging sub wavelength features, and may beespecially useful with emerging imaging technologies capable ofproducing wavelengths of an increasingly smaller size. Emergingtechnologies already in use include EUV (extreme ultra violet)lithography that is capable of producing a 193 nm wavelength with theuse of a ArF laser, and even a 157 nm wavelength with the use of aFluorine laser. Moreover, EUV lithography is capable of producingwavelengths within a range of 20-5 nm by using a synchrotron or byhitting a material (either solid or a plasma) with high energy electronsin order to produce photons within this range. Because most materialsare absorptive within this range, illumination may be produced byreflective mirrors with a multi-stack of Molybdenum and Silicon. Themulti-stack mirror has a 40 layer pairs of Molybdenum and Silicon wherethe thickness of each layer is a quarter wavelength. Even smallerwavelengths may be produced with X-ray lithography. Typically, asynchrotron is used to produce an X-ray wavelength. Since most materialis absorptive at x-ray wavelengths, a thin piece of absorbing materialdefines where features would print (positive resist) or not print(negative resist).

While the concepts disclosed herein may be used for imaging on asubstrate such as a silicon wafer, it shall be understood that thedisclosed concepts may be used with any type of lithographic imagingsystems, e.g., those used for imaging on substrates other than siliconwafers.

The descriptions above are intended to be illustrative, not limiting.Thus, it will be apparent to one skilled in the art that modificationsmay be made to the embodiments as described without departing from thescope of the claims set out below.

What is claimed is:
 1. A computer-implemented method for optimizing alithographic process having an illumination source and a mask, themethod comprising: a free-form optimization process; placingsub-resolution assist feature (SRAF) seeds in a description of the maskbased on a result of the free-form optimization process; and aconstrained optimization process, including growing the SRAF seeds whiletaking into account manufacturability constraints for both theillumination source and the mask, wherein one or more steps areperformed by the computer.
 2. The method of claim 1, wherein thefree-form optimization process includes designing an optimalillumination source that comprises a fully flexible set of illuminationsource points.
 3. The method of claim 2, wherein taking into account themanufacturability constraints for the illumination source includesmatching the optimal illumination source to a diffractive opticalelement.
 4. The method of claim 1, wherein taking into account themanufacturability constraints for the mask includes constraining a masktransmission to a predetermined value.
 5. The method of claim 1, whereinthe constrained optimization process includes iteratively converging acost function, wherein the cost function comprises a function of boththe illumination source and the mask.
 6. The method of claim 5, whereinthe cost function is formulated in terms of one of the following: worstcase edge placement error (EPE) over a given process window, EPE leastsquare function, EPE least p-norm function, inverse NILS p-normfunction, contour integral image slop, edge image value least square,edge image p-norm, and ILS p-norm.
 7. The method of claim 5, where theconvergence of the cost function is accelerated by directly computing agradient of the cost function in each iteration with respect of the maskand the source.
 8. The method of claim 7, wherein the source and themask are reconfigured for each iteration, until the gradient is at adesired minimum value.
 9. A computer program product comprising anon-transitory computer readable medium having instructions recordedthereon, the instructions, when executed by a computer, implements amethod for optimizing a lithographic process having an illuminationsource and a mask, the method comprising: a free-form optimizationprocess; placing sub-resolution assist feature (SRAF) seeds in adescription of the mask based on a result of the free-form optimizationprocess; and a constrained optimization process, including growing theSRAF seeds while taking into account manufacturability constraints forboth the illumination source and the mask.
 10. The computer programproduct of claim 9, wherein the free-form optimization process includesdesigning an optimal illumination source that comprises a fully flexibleset of illumination source points.
 11. The computer program product ofclaim 10, wherein taking into account the manufacturability constraintsfor the illumination source includes matching the optimal illuminationsource to a diffractive optical element.
 12. The computer programproduct of claim 9, wherein taking into account the manufacturabilityconstraints for the mask includes constraining a mask transmission to apredetermined value.
 13. The computer program product of claim 9,wherein the constrained optimization process includes iterativelyconverging a cost function, wherein the cost function comprises afunction of both the illumination source and the mask.
 14. The computerprogram product of claim 13, wherein the cost function is formulated interms of one of the following: worst case edge placement error (EPE)over a given process window, EPE least square function, EPE least p-normfunction, inverse NILS p-norm function, contour integral image slop,edge image value least square, edge image p-norm, and ILS p-norm. 15.The computer program product of claim 13, where the convergence of thecost function is accelerated by directly computing a gradient of thecost function in each iteration with respect of the mask and the source.16. The computer program product of claim 13, wherein the source and themask are reconfigured before each iteration until the gradient is at adesired minimum value.